AI Value Stream Engineer

Position Description: Principal responsibility of processes and performance across value streams. Coaches Agile team and leaders on Agile process during transition, facilitating conversation across agile team leaders. Possesses strong working knowledge of SAFe working methodology. Typically has a bachelor’s degree and successful experience as a Scrum Master. The role also serves as the single point of contact for the State regarding day-to-day IT project operations. The position shall oversee and direct all resources provided under this RFP. His/her responsibilities shall include overall project governance, communications with executives, planning, budgeting, execution, monitoring, control, quality assurance and implementing course corrections as needed.

The AI Value Stream Engineer acts as the primary orchestrator for the AI transformation within the State. You will ensure that AI initiatives—from pilot to production—move through the value stream with minimal friction, integrating DataOps and MLOps principles into the existing SAFe delivery model. You will be responsible for ensuring that AI investments translate into measurable efficiency gains for state agencies.

• Managing day-to-day project activities;
• Identifying issues and risks and recommending possible issue and risk mitigation strategies;
• Facilitating State agency and Contractor discussions / meetings;
• Ensuring that performance is within scope, consistent with requirements, and delivered on time and within budget;
• Identifying critical paths, tasks, dates, testing, and acceptance criteria;
• Ensuring the application of State SDLC standards;
• Providing solutions to improve efficiency (e.g., reduce costs while maintaining or improving performance levels);
• Monitoring issues and providing resolutions for up-to-date status reports; and
• Documenting and delivering project management related artifacts.
• AI Flow Optimization: Identify and remove bottlenecks specifically related to data acquisition, model training, and ethical review cycles within the value stream.
• MLOps Integration: Align the State’s SDLC with Machine Learning pipelines, ensuring that "experimentation" phases do not stall "delivery" phases.
• AI Business Value Mapping: Define and track AI-specific KPIs, such as Inference Cost vs. Value, Model Accuracy over Time, and Task Automation ROI.
• AI Governance Liaison: Work with the AI Security Officer to embed compliance, bias testing, and security guardrails directly into the Agile Release Train (ART).
• Vendor & Model Strategy: Lead the evaluation of AI vendors and open-source models to ensure technical fit within the State’s long-term value stream architecture.

Education: This position requires a Bachelor’s degree from an accredited college or university in a related field and possesses strong working knowledge of SAFe working methodology.

General Experience: The proposed candidate must have at least five (3) years of experience and possesses strong working knowledge of SAFe working methodology and:

• SAFe & AI Synergy: Minimum of 2years of experience in SAFe environments, with a proven ability to adapt Lean-Agile principles to data-heavy or algorithmic projects.
• AI Project Stewardship: Demonstrated experience overseeing the transition of AI/ML projects from "Proof of Concept" (PoC) to full-scale production.
• Strategic Financials: Experience managing budgets that account for the variable costs of cloud AI compute (GPU usage) and API consumption.

Specialized Experience: The proposed candidate must demonstrate at least four (4) years of experience managing complex IT development projects, similar to that described in the Statement of Work. This individual must also have experience in a leadership role for at least two (2) successful projects with an organizational change management component that involve working with stakeholder groups across the organization.

• AI Lifecycle Management: At least 2 years of experience managing complex IT projects specifically involving Natural Language Processing (NLP), Predictive Analytics, or Generative AI.
• Change Management for AI: Led at least two successful projects that involved significant organizational change management regarding the adoption of AI tools by non-technical state staff.
• Technical Fluency: Understanding of the "Data-to-Model-to-App" pipeline, including an ability to facilitate technical discussions between data scientists and infrastructure teams.
• Risk Mitigation for AI: Specific experience identifying and mitigating risks unique to AI, such as Data Drift, Model Hallucinations, and Algorithmic Bias.